Analysing mutation processes in cancer genomes can help early cancer detection and reveal why some cancers become resistant to treatment. The new method outlined by the research team could dramatically accelerate the development of clinical applications that can directly impact future cancer patient care.

It is common practice that patient samples are processed with formalin fixation and paraffin embedding (FFPE) by pathology laboratories around the world. Although this technique may preserve tissue morphology and enable analysis for clinical diagnosis it also degrades the ability for genomic analysis. The DNA extracted from FFPE blocks has been degraded and mutated, because formalin fixation negatively impacts DNA quality and quantity compared to fresh frozen (FF) material.

The ability to precisely characterise mutational signatures from FFPE-derived DNA has tremendous translational potential specifically in cancer. However, sequencing of DNA derived from FFPE material is known to be riddled with inaccuracies.

The study states that that normally nearly half of the cancer processes will be missed without noise correction. However, if the machine learning tool named, FFPEsig, was employed its’ indicated that it can accurately recover the true activities of mutational signatures otherwise masked by FFPE-induced interference.

The method enables robust mutational signature analysis on FFPE samples. So, as the pathology archive of any large hospital is likely to contain tens of thousands of FFPE blocks, there is tremendous translational research potential from these vast collections of archival material that can now undergo accurate genomic analysis.